**GLOW** is a type of flow-based generative model that is based on an invertible $1 \times 1$ [convolution](https://paperswithcode.com/method/convolution). This builds on the flows introduced by [NICE](https://paperswithcode.com/method/nice) and [RealNVP](https://paperswithcode.com/method/realnvp). It consists of a series of steps of flow, combined in a multi-scale architecture; see the Figure to the right. Each step of flow consists of Act Normalization followed by an *invertible $1 \times 1$ convolution* followed by an [affine coupling](https://paperswithcode.com/method/affine-coupling) layer.

**VoiceFilter-Lite** is a single-channel source separation model that runs on the device to preserve only the speech signals from a target user, as part of a streaming speech recognition system. In this architecture, the voice filtering model operates as a frame-by-frame frontend signal processor to enhance the features consumed by the speech recognizer, without reconstructing audio signals from the features. The key contributions are (1) A system to perform speech separation directly on ASR input features; (2) An asymmetric loss function to penalize oversuppression during training, to make the model harmless under various acoustic environments, (3) An adaptive suppression strength mechanism to adapt to different noise conditions.

VoiceFilter-Lite

VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition

GLOW

Glow: Generative Flow with Invertible 1x1 Convolutions

In the OneR method, model input can be one of image, text or image+text, and CMC objective is combined with the traditional image-text contrastive (ITC) loss. Masked modeling is also carried out for all three input types (i.e., image, text and multi-modal). This framework employs no modality-specific architectural component except for the initial token embedding layer, making our model generic and modality-agnostic with minimal inductive bias.

Source	Glow: Generative Flow with Invertible 1x1 Convolutions
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com